
# Libraries to help with reading and manipulating data
import numpy as np
import pandas as pd

# Libraries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

sns.set_theme(style='darkgrid')

# Removes the limit for the number of displayed columns
pd.set_option("display.max_columns", None)
# Sets the limit for the number of displayed rows
pd.set_option("display.max_rows", 200)

# to scale the data using z-score
from sklearn.preprocessing import StandardScaler

# to compute distances
from scipy.spatial.distance import cdist, pdist
# to perform k-means clustering and compute silhouette scores
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

# to visualize the elbow curve and silhouette scores
from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer

# to perform hierarchical clustering, compute cophenetic correlation
from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import dendrogram, linkage, cophenet

# to suppress warnings
import warnings
warnings.filterwarnings("ignore")


# import dataset from google drive

from google.colab import drive
drive.mount('/content/drive')

data = pd.read_csv('/content/drive/MyDrive/stock_data.csv')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).


# Checking first 5 rows of dataset
data.head()


# Checking last 5 rows of dataset
data.tail()


# checking shape of the dataset
data.shape

(340, 15)


# Checking the data types of the columns for the dataset
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 340 entries, 0 to 339
Data columns (total 15 columns):
 #   Column                        Non-Null Count  Dtype  
---  ------                        --------------  -----  
 0   Ticker Symbol                 340 non-null    object 
 1   Security                      340 non-null    object 
 2   GICS Sector                   340 non-null    object 
 3   GICS Sub Industry             340 non-null    object 
 4   Current Price                 340 non-null    float64
 5   Price Change                  340 non-null    float64
 6   Volatility                    340 non-null    float64
 7   ROE                           340 non-null    int64  
 8   Cash Ratio                    340 non-null    int64  
 9   Net Cash Flow                 340 non-null    int64  
 10  Net Income                    340 non-null    int64  
 11  Earnings Per Share            340 non-null    float64
 12  Estimated Shares Outstanding  340 non-null    float64
 13  P/E Ratio                     340 non-null    float64
 14  P/B Ratio                     340 non-null    float64
dtypes: float64(7), int64(4), object(4)
memory usage: 40.0+ KB


# creating a copy of original data
df = data.copy()


# checking for duplicates
df.duplicated().sum()

0


# checking for missing values
df.isnull().sum()


# checking statistical summary of they dataset
df.describe().T


A=df['Security'].unique()
print(A)
B=df['Security'].nunique()
print("Total no. of companies :",B)

['American Airlines Group' 'AbbVie' 'Abbott Laboratories'
 'Adobe Systems Inc' 'Analog Devices, Inc.' 'Archer-Daniels-Midland Co'
 'Alliance Data Systems' 'Ameren Corp' 'American Electric Power'
 'AFLAC Inc' 'American International Group, Inc.'
 'Apartment Investment & Mgmt' 'Assurant Inc' 'Arthur J. Gallagher & Co.'
 'Akamai Technologies Inc' 'Albemarle Corp' 'Alaska Air Group Inc'
 'Allstate Corp' 'Allegion' 'Alexion Pharmaceuticals'
 'Applied Materials Inc' 'AMETEK Inc' 'Affiliated Managers Group Inc'
 'Amgen Inc' 'Ameriprise Financial' 'American Tower Corp A'
 'Amazon.com Inc' 'AutoNation Inc' 'Anthem Inc.' 'Aon plc'
 'Apache Corporation' 'Anadarko Petroleum Corp' 'Amphenol Corp'
 'Arconic Inc' 'Activision Blizzard' 'AvalonBay Communities, Inc.'
 'Broadcom' 'American Water Works Company Inc' 'American Express Co'
 'Boeing Company' 'Bank of America Corp' 'Baxter International Inc.'
 'BB&T Corporation' 'Bard (C.R.) Inc.' 'Baker Hughes Inc'
 'BIOGEN IDEC Inc.' 'The Bank of New York Mellon Corp.' 'Ball Corp'
 'Bristol-Myers Squibb' 'Boston Scientific' 'BorgWarner'
 'Boston Properties' 'Citigroup Inc.' 'Caterpillar Inc.' 'Chubb Limited'
 'CBRE Group' 'Crown Castle International Corp.' 'Carnival Corp.'
 'Celgene Corp.' 'CF Industries Holdings Inc' 'Citizens Financial Group'
 'Church & Dwight' 'Chesapeake Energy' 'C. H. Robinson Worldwide'
 'Charter Communications' 'CIGNA Corp.' 'Cincinnati Financial'
 'Colgate-Palmolive' 'Comerica Inc.' 'CME Group Inc.'
 'Chipotle Mexican Grill' 'Cummins Inc.' 'CMS Energy'
 'Centene Corporation' 'CenterPoint Energy' 'Capital One Financial'
 'Cabot Oil & Gas' 'The Cooper Companies' 'CSX Corp.' 'CenturyLink Inc'
 'Cognizant Technology Solutions' 'Citrix Systems' 'CVS Health'
 'Chevron Corp.' 'Concho Resources' 'Dominion Resources' 'Delta Air Lines'
 'Du Pont (E.I.)' 'Deere & Co.' 'Discover Financial Services'
 'Quest Diagnostics' 'Danaher Corp.' 'The Walt Disney Company'
 'Discovery Communications-A' 'Discovery Communications-C'
 'Delphi Automotive' 'Digital Realty Trust' 'Dun & Bradstreet'
 'Dover Corp.' 'Dr Pepper Snapple Group' 'Duke Energy' 'DaVita Inc.'
 'Devon Energy Corp.' 'eBay Inc.' 'Ecolab Inc.' 'Consolidated Edison'
 'Equifax Inc.' "Edison Int'l" 'Eastman Chemical' 'EOG Resources'
 'Equinix' 'Equity Residential' 'EQT Corporation' 'Eversource Energy'
 'Essex Property Trust, Inc.' 'E*Trade' 'Eaton Corporation'
 'Entergy Corp.' 'Edwards Lifesciences' 'Exelon Corp.' "Expeditors Int'l"
 'Expedia Inc.' 'Extra Space Storage' 'Ford Motor' 'Fastenal Co'
 'Facebook' 'Fortune Brands Home & Security' 'Freeport-McMoran Cp & Gld'
 'FirstEnergy Corp' 'Fidelity National Information Services' 'Fiserv Inc'
 'FLIR Systems' 'Fluor Corp.' 'Flowserve Corporation' 'FMC Corporation'
 'Federal Realty Investment Trust' 'First Solar Inc'
 'Frontier Communications' 'General Dynamics'
 'General Growth Properties Inc.' 'Gilead Sciences' 'Corning Inc.'
 'General Motors' 'Genuine Parts' 'Garmin Ltd.' 'Goodyear Tire & Rubber'
 'Grainger (W.W.) Inc.' 'Halliburton Co.' 'Hasbro Inc.'
 'Huntington Bancshares' 'HCA Holdings' 'Welltower Inc.' 'HCP Inc.'
 'Hess Corporation' 'Hartford Financial Svc.Gp.' 'Harley-Davidson'
 "Honeywell Int'l Inc." 'Hewlett Packard Enterprise' 'HP Inc.'
 'Hormel Foods Corp.' 'Henry Schein' 'Host Hotels & Resorts'
 'The Hershey Company' 'Humana Inc.' 'International Business Machines'
 'IDEXX Laboratories' 'Intl Flavors & Fragrances' 'Intel Corp.'
 'International Paper' 'Interpublic Group' 'Iron Mountain Incorporated'
 'Intuitive Surgical Inc.' 'Illinois Tool Works' 'Invesco Ltd.'
 'J. B. Hunt Transport Services' 'Jacobs Engineering Group'
 'Juniper Networks' 'JPMorgan Chase & Co.' 'Kimco Realty' 'Kimberly-Clark'
 'Kinder Morgan' 'Coca Cola Company' 'Kansas City Southern'
 'Leggett & Platt' 'Lennar Corp.' 'Laboratory Corp. of America Holding'
 'LKQ Corporation' 'L-3 Communications Holdings' 'Lilly (Eli) & Co.'
 'Lockheed Martin Corp.' 'Alliant Energy Corp' 'Leucadia National Corp.'
 'Southwest Airlines' 'Level 3 Communications' 'LyondellBasell'
 'Mastercard Inc.' 'Mid-America Apartments' 'Macerich' "Marriott Int'l."
 'Masco Corp.' 'Mattel Inc.' "McDonald's Corp." "Moody's Corp"
 'Mondelez International' 'MetLife Inc.' 'Mohawk Industries'
 'Mead Johnson' 'McCormick & Co.' 'Martin Marietta Materials'
 'Marsh & McLennan' '3M Company' 'Monster Beverage' 'Altria Group Inc'
 'The Mosaic Company' 'Marathon Petroleum' 'Merck & Co.'
 'Marathon Oil Corp.' 'M&T Bank Corp.' 'Mettler Toledo' 'Murphy Oil'
 'Mylan N.V.' 'Navient' 'Noble Energy Inc' 'NASDAQ OMX Group'
 'NextEra Energy' 'Newmont Mining Corp. (Hldg. Co.)' 'Netflix Inc.'
 'Newfield Exploration Co' 'Nielsen Holdings'
 'National Oilwell Varco Inc.' 'Norfolk Southern Corp.'
 'Northern Trust Corp.' 'Nucor Corp.' 'Newell Brands'
 'Realty Income Corporation' 'ONEOK' 'Omnicom Group' "O'Reilly Automotive"
 'Occidental Petroleum' "People's United Financial" 'Pitney-Bowes'
 'PACCAR Inc.' 'PG&E Corp.' 'Priceline.com Inc'
 'Public Serv. Enterprise Inc.' 'PepsiCo Inc.' 'Pfizer Inc.'
 'Principal Financial Group' 'Procter & Gamble' 'Progressive Corp.'
 'Pulte Homes Inc.' 'Philip Morris International' 'PNC Financial Services'
 'Pentair Ltd.' 'Pinnacle West Capital' 'PPG Industries' 'PPL Corp.'
 'Prudential Financial' 'Phillips 66' 'Quanta Services Inc.'
 'Praxair Inc.' 'PayPal' 'Ryder System' 'Royal Caribbean Cruises Ltd'
 'Regeneron' 'Robert Half International' 'Roper Industries'
 'Range Resources Corp.' 'Republic Services Inc' 'SCANA Corp'
 'Charles Schwab Corporation' 'Spectra Energy Corp.' 'Sealed Air'
 'Sherwin-Williams' 'SL Green Realty' 'Scripps Networks Interactive Inc.'
 'Southern Co.' 'Simon Property Group Inc' 'S&P Global, Inc.'
 'Stericycle Inc' 'Sempra Energy' 'SunTrust Banks' 'State Street Corp.'
 'Skyworks Solutions' 'Southwestern Energy' 'Synchrony Financial'
 'Stryker Corp.' 'AT&T Inc' 'Molson Coors Brewing Company'
 'Teradata Corp.' 'Tegna, Inc.' 'Torchmark Corp.'
 'Thermo Fisher Scientific' 'TripAdvisor' 'The Travelers Companies Inc.'
 'Tractor Supply Company' 'Tyson Foods' 'Tesoro Petroleum Co.'
 'Total System Services' 'Texas Instruments' 'Under Armour'
 'United Continental Holdings' 'UDR Inc' 'Universal Health Services, Inc.'
 'United Health Group Inc.' 'Unum Group' 'Union Pacific'
 'United Parcel Service' 'United Technologies' 'Varian Medical Systems'
 'Valero Energy' 'Vulcan Materials' 'Vornado Realty Trust'
 'Verisk Analytics' 'Verisign Inc.' 'Vertex Pharmaceuticals Inc'
 'Ventas Inc' 'Verizon Communications' 'Waters Corporation'
 'Wec Energy Group Inc' 'Wells Fargo' 'Whirlpool Corp.'
 'Waste Management Inc.' 'Williams Cos.' 'Western Union Co'
 'Weyerhaeuser Corp.' 'Wyndham Worldwide' 'Wynn Resorts Ltd'
 'Cimarex Energy' 'Xcel Energy Inc' 'XL Capital' 'Exxon Mobil Corp.'
 'Dentsply Sirona' 'Xerox Corp.' 'Xylem Inc.' 'Yahoo Inc.'
 'Yum! Brands Inc' 'Zimmer Biomet Holdings' 'Zions Bancorp' 'Zoetis']
Total no. of companies : 340


#  creating a histogram for current price

plt.figure(figsize=(8, 6))  # Set the figure size
sns.histplot(df['Current Price'], kde=True,)
plt.title('Distribution of Current Stock Prices')  # Set the title
plt.xlabel('Current Price')  # Set the x-axis label
plt.ylabel ('Frequency')  # Set the y-axis label
plt.show()  # Display the plot


# creating a boxplot of current price
plt.figure(figsize=(8, 5))  # Set the figure size
sns.boxplot(x=df['Current Price'])
plt.title('Boxplot of Current Stock  Prices')  # Set the title
plt.xlabel('Current Price')  # Set the x-axis label
plt.show()  # Display the plot


# Create a histogram for Volatility
plt.figure(figsize=(8, 5))  # Set the figure size
sns.histplot(df['Volatility'], kde=True)
plt.title('Distribution of Volatility')  # Set the title
plt.xlabel('Volatility')  # Set the x-axis label
plt.ylabel('Frequency')  # Set the y-axis label
plt.show()  # Display the plot


# creating a boxplot of volatility
plt.figure(figsize=(8, 5))  # Set the figure size
sns.boxplot(x=df['Volatility'])
plt.title('Boxplot of Volatility')  # Set the title
plt.xlabel('Volatility')  # Set the x-axis label
plt.show()  # Display the plot


# Create a timeline for volatility
plt.figure(figsize=(15, 6))
plt.plot(df['Ticker Symbol'], df['Volatility'])
plt.xlabel('Ticker Symbol')  # Set the x-axis label
plt.ylabel('Volatility')  # Set the y-axis label
plt.title('Volatility Timeline')  # Set the title
plt.xticks(rotation=90)  # Rotate x-axis labels for better readability
plt.tight_layout() # Adjust layout to prevent labels from overlapping
plt.show()


#  create histogram of ROE

plt.figure(figsize=(8, 4))  # Set the figure size
sns.histplot(df['ROE'], kde=True)
plt.title('Distribution of ROE')  # Set the title
plt.xlabel('ROE')  # Set the x-axis label
plt.ylabel('Frequency')  # Set the y-axis label
plt.show()  # Display the plot


#create boxplot of ROE

plt.figure(figsize=(8, 4))  # Set the figure size
sns.boxplot(x=df['ROE'])
plt.title('Boxplot of ROE')  # Set the title
plt.xlabel('ROE')  # Set the x-axis label
plt.show()  # Display the plot


#  generating histogram of cash ratio for analysis
plt.figure(figsize=(6, 4))  # Set the figure size
sns.histplot(df['Cash Ratio'], kde=True)
plt.title('Distribution of Cash Ratio')  # Set the title
plt.xlabel('Cash Ratio')  # Set the x-axis label
plt.ylabel('Frequency')  # Set the y-axis label
plt.show()  # Display the plot


# generating boxplot of cash ratio for analysis
plt.figure(figsize=(6, 4))  # Set the figure size
sns.boxplot(x=df['Cash Ratio'])
plt.title('Boxplot of Cash Ratio')  # Set the title
plt.xlabel('Cash Ratio')  # Set the x-axis label
plt.show()  # Display the plot


# generating histogram of net cash flow for analysis
plt.figure(figsize=(8, 4))  # Set the figure size
sns.histplot(df['Net Cash Flow'], kde=True)
plt.title('Distribution of Net Cash Flow')  # Set the title
plt.xlabel('Net Cash Flow')  # Set the x-axis label
plt.ylabel('Frequency')  # Set the y-axis label
plt.show()  # Display the plot


# generating boxplot of net cash flow for analysis
plt.figure(figsize=(8, 4))  # Set the figure size
sns.boxplot(x=df['Net Cash Flow'])
plt.title('Boxplot of Net Cash Flow')  # Set the title
plt.xlabel('Net Cash Flow')  # Set the x-axis label
plt.show()  # Display the plot


# generating boxplot of net income for analysis
plt.figure(figsize=(7, 4))  # Set the figure size
sns.boxplot(x=df['Net Income'])
plt.title('Boxplot of Net Income')  # Set the title
plt.xlabel('Net Income')  # Set the x-axis label
plt.show()  # Display the plot


# generating histogram of net income for analysis
plt.figure(figsize=(6, 4))  # Set the figure size
sns.histplot(df['Net Income'], kde=True)
plt.title('Distribution of Net Income')  # Set the title
plt.xlabel('Net Income')  # Set the x-axis label
plt.ylabel('Frequency')  # Set the y-axis label
plt.show()  # Display the plot


#  generating histogram of earning per share for analysis
plt.figure(figsize=(8, 6))  # Set the figure size
sns.histplot(df['Earnings Per Share'], kde=True)
plt.title('Distribution of Earnings Per Share')  # Set the title
plt.xlabel('Earnings Per Share')  # Set the x-axis label
plt.ylabel('Frequency')  # Set the y-axis label
plt.show()  # Display the plot


# generating boxplot of earning per share for analysis
plt.figure(figsize=(8, 6))  # Set the figure size
sns.boxplot(x=df['Earnings Per Share'])
plt.title('Boxplot of Earnings Per Share')  # Set the title
plt.xlabel('Earnings Per Share')  # Set the x-axis label
plt.show()  # Display the plot


# generating histogram of estimated shares outstanding for analysis
plt.figure(figsize=(8, 6))  # Set the figure size
sns.histplot(df['Estimated Shares Outstanding'], kde=True)
plt.title('Distribution of Estimated Shares Outstanding')  # Set the title
plt.xlabel('Estimated Shares Outstanding')  # Set the x-axis label
plt.ylabel('Frequency')  # Set the y-axis label
plt.show()  # Display the plot


# generating boxplot of estimated shares outstanding for analysis
plt.figure(figsize=(8, 6))  # Set the figure size
sns.boxplot(x=df['Estimated Shares Outstanding'])
plt.title('Boxplot of Estimated Shares Outstanding')  # Set the title
plt.xlabel('Estimated Shares Outstanding')  # Set the x-axis label
plt.show()  # Display the plot


# generating histogram of P/E ratio for analysis
plt.figure(figsize=(6, 5))  # Set the figure size
sns.histplot(df['P/E Ratio'], kde=True)
plt.title('Distribution of P/E Ratio')  # Set the title
plt.xlabel('P/E Ratio')  # Set the x-axis label
plt.ylabel('Frequency')  # Set the y-axis label
plt.show()  # Display the plot


# generating boxplot of P/E ratio for analysis
plt.figure(figsize=(6, 6))  # Set the figure size
sns.boxplot(x=df['P/E Ratio'])
plt.title('Boxplot of P/E Ratio')  # Set the title
plt.xlabel('P/E Ratio')  # Set the x-axis label
plt.show()  # Display the plot


# generating histogram of P/B ratio for analysis
plt.figure(figsize=(6, 5))  # Set the figure size
sns.histplot(df['P/B Ratio'], kde=True)
plt.title('Distribution of P/B Ratio')  # Set the title
plt.xlabel('P/B Ratio')  # Set the x-axis label
plt.ylabel('Frequency')  # Set the y-axis label
plt.show()  # Display the plot


# generating boxplot of P/B ratio for analysis
plt.figure(figsize=(6, 6))  # Set the figure size
sns.boxplot(x=df['P/B Ratio'])
plt.title('Boxplot of P/B Ratio')  # Set the title
plt.xlabel('P/B Ratio')  # Set the x-axis label
plt.show()  # Display the plot


# Create a bar plot for GICS Sector
plt.figure(figsize=(18, 6))  # Set the figure size
sns.countplot(x='GICS Sector', data=df)
plt.title('Distribution of GICS Sectors')  # Set the title
plt.xlabel('GICS Sector')  # Set the x-axis label
plt.ylabel('Count')  # Set the y-axis label
plt.xticks(rotation=45, ha='right') # Rotate x-axis labels for better readability
plt.tight_layout()  # Adjust layout to prevent labels from overlapping
plt.show()  # Display the plot


# creating a barplot of gics sub industry

plt.figure(figsize=(12, 6))  # Set the figure size
sns.countplot(x='GICS Sub Industry', data=df)  # Create a countplot
plt.title('Distribution of GICS Sub Industries')  # Set the title
plt.xlabel('Count')  # Set the x-axis label
plt.ylabel('GICS Sub Industry')  # Set the y-axis label
plt.xticks(rotation=90, ha='right')  # Rotate x-axis labels for better readability
plt.tight_layout()  # Adjust layout to prevent labels from overlapping
plt.show()  # Display the plot


# check correlation using heatmap
# Select the columns for correlation analysis
columns_for_correlation = ['Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio', 'Net Cash Flow',
                           'Net Income', 'Earnings Per Share', 'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio']

# Create a heatmap
plt.figure(figsize=(12, 8))  # Adjust figure size as needed
correlation_matrix = df[columns_for_correlation].corr()  # Calculate correlation matrix
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5)  # Create heatmap
plt.title('Correlation Heatmap')  # Set the title
plt.show()


# generating scatterplot for Net Income vs. Earnings Per Share (EPS)

plt.figure(figsize=(6, 6))  # Set the figure size
sns.scatterplot(x='Net Income', y='Earnings Per Share', data=df)
plt.title('Net Income vs. Earnings Per Share')  # Set the title
plt.xlabel('Net Income')  # Set the x-axis label
plt.ylabel('Earnings Per Share')  # Set the y-axis label
plt.show()  # Display the plot


# generating scatterplot for Earnings Per Share vs. Current Price

plt.figure(figsize=(6, 6))  # Set the figure size
sns.scatterplot(x='Earnings Per Share', y='Current Price', data=df)
plt.title('Earnings Per Share vs. Current Price')  # Set the title
plt.xlabel('Earnings Per Share')  # Set the x-axis label
plt.ylabel('Current Price')  # Set the y-axis label
plt.show()  # Display the plot


# generating scatterplot for Net Income vs. Estimated Shares Outstanding
plt.figure(figsize=(6, 6))  # Set the figure size
sns.scatterplot(x='Net Income', y='Estimated Shares Outstanding', data=df)
plt.title('Net Income vs. Estimated Shares Outstanding')  # Set the title
plt.xlabel('Net Income')  # Set the x-axis label
plt.ylabel('Estimated Shares Outstanding')  # Set the y-axis label
plt.show()  # Display the plot


# analysing using scatterplot of Volatility vs. Price Change

plt.figure(figsize=(6, 6))  # Set the figure size
sns.scatterplot(x='Volatility', y='Price Change', data=df)
plt.title('Volatility vs. Price Change')  # Set the title
plt.xlabel('Volatility')  # Set the x-axis label
plt.ylabel('Price Change')  # Set the y-axis label
plt.show()  # Display the plot


# generating scatterplot of ROE vs. Earnings Per Share

plt.figure(figsize=(6, 6))  # Set the figure size
sns.scatterplot(x='ROE', y='Earnings Per Share', data=df)
plt.title('ROE vs. Earnings Per Share')  # Set the title
plt.xlabel('ROE')  # Set the x-axis label
plt.ylabel('Earnings Per Share')  # Set the y-axis label
plt.show()  # Display the plot


# generating scatterplot of  Current Price vs. P/E Ratio

plt.figure(figsize=(6, 6))  # Set the figure size
sns.scatterplot(x='Current Price', y='P/E Ratio', data=df)
plt.title('Current Price vs. P/E Ratio')  # Set the title
plt.xlabel('Current Price')  # Set the x-axis label
plt.ylabel('P/E Ratio')  # Set the y-axis label
plt.show()  # Display the plot


# generate a scatterplot of P/B Ratio vs. Cash Ratio

plt.figure(figsize=(6, 6))  # Set the figure size
sns.scatterplot(x='P/B Ratio', y='Cash Ratio', data=df)
plt.title('P/B Ratio vs. Cash Ratio')  # Set the title
plt.xlabel('P/B Ratio')  # Set the x-axis label
plt.ylabel('Cash Ratio')  # Set the y-axis label
plt.show()  # Display the plot


# Generate boxplot of Volatility vs. GICS Sector
plt.figure(figsize=(12, 6))  # Set the figure size
sns.boxplot(x='GICS Sector', y='Volatility', data=df)
plt.title('Volatility vs. GICS Sector')
plt.xlabel('GICS Sector')
plt.ylabel('Volatility') # set y label
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()


# analyzing using scatterplot of Net Cash Flow vs. Cash Ratio

plt.figure(figsize=(6, 6))  # Set the figure size
sns.scatterplot(x='Net Cash Flow', y='Cash Ratio', data=df)
plt.title('Net Cash Flow vs. Cash Ratio')  # Set the title
plt.xlabel('Net Cash Flow')  # Set the x-axis label
plt.ylabel('Cash Ratio')  # Set the y-axis label
plt.show()  # Display the plot


#  generate boxplot of  Price Change vs. GICS Sector

plt.figure(figsize=(12, 6))  # Set the figure size
sns.boxplot(x='GICS Sector', y='Price Change', data=df)
plt.title('Price Change vs. GICS Sector') # title of the plot
plt.xlabel('GICS Sector')  # sey x axis label
plt.ylabel('Price Change') # set y label
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.show()


#  scatterplot of P/E Ratio vs. Volatility

plt.figure(figsize=(6, 6))  # Set the figure size
sns.scatterplot(x='P/E Ratio', y='Volatility', data=df)
plt.title('P/E Ratio vs. Volatility')  # Set the title
plt.xlabel('P/E Ratio')  # Set the x-axis label
plt.ylabel('Volatility')  # Set the y-axis label
plt.show()  # Display the plot


# create a scatterplot of Price Change vs. ROE

plt.figure(figsize=(6, 6))  # Set the figure size
sns.scatterplot(x='Price Change', y='ROE', data=df)
plt.title('Price Change vs. ROE')  # Set the title
plt.xlabel('Price Change')  # Set the x-axis label
plt.ylabel('ROE')  # Set the y-axis label
plt.show()  # Display the plot


# generate barplot for price change and gisc sector
plt.figure(figsize=(15,8))  # Set the figure size
sns.barplot(data=df, x='GICS Sector', y='Price Change', ci=False)
plt.xticks(rotation=90)  # Rotate x-axis labels for better readability
plt.title('Price Change by GICS Sector')  # Set the title
plt.xlabel('GICS Sector')  # Set the x-axis label
plt.ylabel('Price Change')  # Set the y-axis label
plt.show()


# generate a barplot of P/E ratio and sics sector
plt.figure(figsize=(15,8))  # Set the figure size
sns.barplot(data=df, x='GICS Sector', y='P/E Ratio', ci=False)
plt.xticks(rotation=90)  # Rotate x-axis labels for better readability
plt.title('P/E Ratio by GICS Sector')  # Set the title
plt.xlabel('GICS Sector')  # Set the x-axis label
plt.ylabel('P/E Ratio')
plt.show()


# generate a barplot of volatility and gics sector
plt.figure(figsize=(15,8))  # Set the figure size
sns.barplot(data=df, x='GICS Sector', y='Volatility', ci=False)
plt.xticks(rotation=90)  # Rotate x-axis labels for better readability
plt.title('Volatility by GICS Sector')  # Set the title
plt.xlabel('GICS Sector')  # Set the x-axis label
plt.ylabel('Volatility')
plt.show()


# prompt: 3d scatterplot of Current Price, Earnings Per Share (EPS), and P/E Ratio

import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# Assuming 'df' is your DataFrame
fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, projection='3d')

x = df['Current Price']
y = df['Earnings Per Share']
z = df['P/E Ratio']

ax.scatter(x, y, z, c=z, cmap='viridis')  # Use colormap for better visualization

ax.set_xlabel('Current Price')
ax.set_ylabel('Earnings Per Share (EPS)')
ax.set_zlabel('P/E Ratio')
ax.set_title('3D Scatterplot of Current Price, EPS, and P/E Ratio')

plt.show()


#  3d scatterplot of ROE, Cash Ratio, and Net Income


fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, projection='3d')

x = df['ROE']
y = df['Cash Ratio']
z = df['Net Income']

ax.scatter(x, y, z, c=z, cmap='viridis')  # Use colormap for better visualization

ax.set_xlabel('ROE')
ax.set_ylabel('Cash Ratio')
ax.set_zlabel('Net Income')
ax.set_title('3D Scatterplot of ROE, Cash Ratio, and Net Income')

plt.show()


# generate pairplot of Net Cash Flow, Cash Ratio, and P/B Ratio
plt.figure(figsize=(8, 6))  # Set the figure size
sns.pairplot(df[['Net Cash Flow', 'Cash Ratio', 'P/B Ratio']])
plt.suptitle('Pairplot of Net Cash Flow, Cash Ratio, and P/B Ratio')  # Set the title
plt.show()  # Display the plot

<Figure size 800x600 with 0 Axes>


#  generate a Volatility, ROE, and Net Income


fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, projection='3d')

x = df['Volatility']
y = df['ROE']
z = df['Net Income']

ax.scatter(x, y, z, c=z, cmap='viridis')

ax.set_xlabel('Volatility')
ax.set_ylabel('ROE')
ax.set_zlabel('Net Income')
ax.set_title('3D Scatterplot of Volatility, ROE, and Net Income')

plt.show()


# box plot of GICS Sector, Price Change, and Current Price

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(12, 6))  # Adjust figure size as needed
sns.barplot(hue='GICS Sector', x='Price Change', y='Current Price', data=df)
plt.title('GICS Sector vs Price Change (Hue: Current Price)')  # Set the title
plt.xlabel('current price')  # Set the x-axis label
plt.ylabel('Price Change')  # Set the y-axis label
plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability
plt.tight_layout()
plt.show()


# Question 1: Distribution of Stock Prices
plt.figure(figsize=(5, 5))
sns.histplot(df['Current Price'], kde=True, bins=30)
plt.title('Distribution of Stock Prices')
plt.xlabel('Stock Price')
plt.ylabel('Frequency')
plt.show()


avg_price_change_by_sector = df.groupby('GICS Sector')['Price Change'].mean().sort_values(ascending=False)

# Display the sector with the highest average price change
avg_price_change_by_sector


# check correlation using heatmap
# Select the columns for correlation analysis
columns_for_correlation = ['Current Price', 'Price Change', 'Volatility', 'ROE', 'Cash Ratio', 'Net Cash Flow',
                           'Net Income', 'Earnings Per Share', 'Estimated Shares Outstanding', 'P/E Ratio', 'P/B Ratio']

# Create a heatmap
plt.figure(figsize=(8, 8))  # Adjust figure size as needed
correlation_matrix = df[columns_for_correlation].corr()  # Calculate correlation matrix
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f", linewidths=.5)  # Create heatmap
plt.title('Correlation Heatmap')  # Set the title
plt.show()


# Question 4: Calculate average cash ratio across economic sectors
avg_cash_ratio_by_sector = data.groupby('GICS Sector')['Cash Ratio'].mean()

# Display the average cash ratio by sector
avg_cash_ratio_by_sector


# Question 5: Calculate average P/E ratio across economic sectors
avg_pe_ratio_by_sector = data.groupby('GICS Sector')['P/E Ratio'].mean()

# Display results
avg_pe_ratio_by_sector


# prompt: check duplicate values'

# Check for duplicate rows
duplicate_rows = df[df.duplicated()]

# Print the number of duplicate rows
print(f"Number of duplicate rows: {len(duplicate_rows)}")

Number of duplicate rows: 0


# Check for missing values in the DataFrame
missing_values = df.isnull().sum()

# Print the number of missing values for each column
missing_values


plt.figure(figsize=(15, 12))

numeric_columns = df.select_dtypes(include=np.number).columns.tolist()

for i, variable in enumerate(numeric_columns):
    plt.subplot(3, 4, i + 1)
    plt.boxplot(df[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()


# scaling the data before clustering
scaler = StandardScaler()
subset = df[numeric_columns].copy()
subset_scaled = scaler.fit_transform(subset)


# creating a dataframe of the scaled columns
subset_scaled_df = pd.DataFrame(subset_scaled, columns=subset.columns)


subset_scaled_df.head()


k_means_df = subset_scaled_df.copy()


clusters = range(1, 15)
meanDistortions = []

for k in clusters:
    model = KMeans(n_clusters=k, random_state=1)
    model.fit(subset_scaled_df)
    prediction = model.predict(k_means_df)
    distortion = (
        sum(np.min(cdist(k_means_df, model.cluster_centers_, "euclidean"), axis=1))
        / k_means_df.shape[0]
         )

    meanDistortions.append(distortion)

    print("Number of Clusters:", k, "\tAverage Distortion:", distortion)

plt.plot(clusters, meanDistortions, "bx-")
plt.xlabel("k")
plt.ylabel("Average Distortion")
plt.title("Selecting k with the Elbow Method", fontsize=20)
plt.show()

Number of Clusters: 1 	Average Distortion: 2.5425069919221697
Number of Clusters: 2 	Average Distortion: 2.3862098789299604
Number of Clusters: 3 	Average Distortion: 2.33620927590848
Number of Clusters: 4 	Average Distortion: 2.219050563833442
Number of Clusters: 5 	Average Distortion: 2.133404401901685
Number of Clusters: 6 	Average Distortion: 2.081503686093715
Number of Clusters: 7 	Average Distortion: 2.0045413402786814
Number of Clusters: 8 	Average Distortion: 1.9864237824874411
Number of Clusters: 9 	Average Distortion: 1.956222103389025
Number of Clusters: 10 	Average Distortion: 1.9360473996664198
Number of Clusters: 11 	Average Distortion: 1.8615942883461607
Number of Clusters: 12 	Average Distortion: 1.8219574388532505
Number of Clusters: 13 	Average Distortion: 1.7936924742607907
Number of Clusters: 14 	Average Distortion: 1.7567842179093438


model = KMeans(random_state=1)
visualizer = KElbowVisualizer(model, k=(1, 15), timings=True)
visualizer.fit(k_means_df)  # fit the data to the visualizer
visualizer.show()  # finalize and render figure

<Axes: title={'center': 'Distortion Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='distortion score'>


sil_score = []
cluster_list = range(2, 15)
for n_clusters in cluster_list:
    clusterer = KMeans(n_clusters=n_clusters, random_state=1)
    preds = clusterer.fit_predict((subset_scaled_df))
    score = silhouette_score(k_means_df, preds)
    sil_score.append(score)
    print("For n_clusters = {}, the silhouette score is {})".format(n_clusters, score))

plt.plot(cluster_list, sil_score)
plt.show()

For n_clusters = 2, the silhouette score is 0.45335782729503565)
For n_clusters = 3, the silhouette score is 0.40374060030338865)
For n_clusters = 4, the silhouette score is 0.4246430808437099)
For n_clusters = 5, the silhouette score is 0.4381539778147092)
For n_clusters = 6, the silhouette score is 0.40869599703024256)
For n_clusters = 7, the silhouette score is 0.1207450219233897)
For n_clusters = 8, the silhouette score is 0.3693991650696542)
For n_clusters = 9, the silhouette score is 0.35185096182499204)
For n_clusters = 10, the silhouette score is 0.32950073703610283)
For n_clusters = 11, the silhouette score is 0.1486586842527321)
For n_clusters = 12, the silhouette score is 0.15784241071085106)
For n_clusters = 13, the silhouette score is 0.15646997458716602)
For n_clusters = 14, the silhouette score is 0.16253506827999134)


model = KMeans(random_state=1)
visualizer = KElbowVisualizer(model, k=(2, 15), metric="silhouette", timings=True)
visualizer.fit(k_means_df)  # fit the data to the visualizer
visualizer.show()  # finalize and render figure

<Axes: title={'center': 'Silhouette Score Elbow for KMeans Clustering'}, xlabel='k', ylabel='silhouette score'>


# finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(6, random_state=1))  ## Complete the code to visualize the silhouette scores for certain number of clusters
visualizer.fit(k_means_df)
visualizer.show()

<Axes: title={'center': 'Silhouette Plot of KMeans Clustering for 340 Samples in 6 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>


# finding optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(9, random_state=1))
visualizer.fit(subset_scaled_df)
visualizer.show()

<Axes: title={'center': 'Silhouette Plot of KMeans Clustering for 340 Samples in 9 Centers'}, xlabel='silhouette coefficient values', ylabel='cluster label'>


# final K-means model
kmeans = KMeans(n_clusters=9, random_state=1)  ## Complete the code to choose the number of clusters
kmeans.fit(k_means_df)

KMeans(n_clusters=9, random_state=1)

KMeans(n_clusters=9, random_state=1)


# creating a copy of the original data
df1 = df.copy()

# adding kmeans cluster labels to the original and scaled dataframes
k_means_df["KM_segments"] = kmeans.labels_
df1["KM_segments"] = kmeans.labels_


# Select only numeric columns
km_cluster_profile = df1.groupby("KM_segments").mean(numeric_only=True)


# Filter only numeric columns manually
km_cluster_profile = df1.select_dtypes(include='number').groupby(df1["KM_segments"]).mean()


km_cluster_profile["count_in_each_segment"] = (
    df1.groupby("KM_segments")["Security"].count().values )


km_cluster_profile.style.highlight_max(color="lightgreen", axis=0)


for cl in df1["KM_segments"].unique():
    print("In cluster {}, the following companies are present:".format(cl))
    print(df1[df1["KM_segments"] == cl]["Security"].unique())
    print()

In cluster 6, the following companies are present:
['American Airlines Group' 'AbbVie' 'Allegion'
 'Affiliated Managers Group Inc' 'Anthem Inc.' 'Charter Communications'
 'Colgate-Palmolive' 'CME Group Inc.' 'Discovery Communications-A'
 'Discovery Communications-C' 'Expedia Inc.' 'IDEXX Laboratories'
 'Kimberly-Clark' 'Lockheed Martin Corp.' 'Masco Corp.' 'Altria Group Inc'
 'Pitney-Bowes' 'S&P Global, Inc.' 'United Continental Holdings'
 'United Parcel Service' 'Whirlpool Corp.']

In cluster 2, the following companies are present:
['Abbott Laboratories' 'Adobe Systems Inc' 'Archer-Daniels-Midland Co'
 'Ameren Corp' 'American Electric Power' 'AFLAC Inc'
 'American International Group, Inc.' 'Apartment Investment & Mgmt'
 'Assurant Inc' 'Arthur J. Gallagher & Co.' 'Akamai Technologies Inc'
 'Albemarle Corp' 'Alaska Air Group Inc' 'Allstate Corp'
 'Applied Materials Inc' 'AMETEK Inc' 'Ameriprise Financial'
 'American Tower Corp A' 'AutoNation Inc' 'Aon plc' 'Amphenol Corp'
 'Activision Blizzard' 'AvalonBay Communities, Inc.' 'Broadcom'
 'American Water Works Company Inc' 'American Express Co' 'Boeing Company'
 'Baxter International Inc.' 'BB&T Corporation' 'Bard (C.R.) Inc.'
 'The Bank of New York Mellon Corp.' 'Ball Corp' 'Bristol-Myers Squibb'
 'Boston Scientific' 'BorgWarner' 'Boston Properties' 'Caterpillar Inc.'
 'Chubb Limited' 'CBRE Group' 'Crown Castle International Corp.'
 'Carnival Corp.' 'Citizens Financial Group' 'Church & Dwight'
 'C. H. Robinson Worldwide' 'CIGNA Corp.' 'Cincinnati Financial'
 'Comerica Inc.' 'Cummins Inc.' 'CMS Energy' 'Centene Corporation'
 'CenterPoint Energy' 'Capital One Financial' 'The Cooper Companies'
 'CSX Corp.' 'CenturyLink Inc' 'Cognizant Technology Solutions'
 'Citrix Systems' 'CVS Health' 'Chevron Corp.' 'Dominion Resources'
 'Delta Air Lines' 'Du Pont (E.I.)' 'Deere & Co.'
 'Discover Financial Services' 'Quest Diagnostics' 'Danaher Corp.'
 'The Walt Disney Company' 'Delphi Automotive' 'Digital Realty Trust'
 'Dun & Bradstreet' 'Dover Corp.' 'Dr Pepper Snapple Group' 'Duke Energy'
 'DaVita Inc.' 'Ecolab Inc.' 'Consolidated Edison' 'Equifax Inc.'
 "Edison Int'l" 'Eastman Chemical' 'Equity Residential'
 'Eversource Energy' 'Essex Property Trust, Inc.' 'E*Trade'
 'Eaton Corporation' 'Entergy Corp.' 'Exelon Corp.' "Expeditors Int'l"
 'Extra Space Storage' 'Fastenal Co' 'Fortune Brands Home & Security'
 'FirstEnergy Corp' 'Fidelity National Information Services' 'Fiserv Inc'
 'FLIR Systems' 'Fluor Corp.' 'Flowserve Corporation' 'FMC Corporation'
 'Federal Realty Investment Trust' 'General Dynamics'
 'General Growth Properties Inc.' 'Corning Inc.' 'General Motors'
 'Genuine Parts' 'Garmin Ltd.' 'Goodyear Tire & Rubber'
 'Grainger (W.W.) Inc.' 'Hasbro Inc.' 'Huntington Bancshares'
 'HCA Holdings' 'Welltower Inc.' 'HCP Inc.' 'Hartford Financial Svc.Gp.'
 'Harley-Davidson' "Honeywell Int'l Inc." 'HP Inc.' 'Hormel Foods Corp.'
 'Henry Schein' 'Host Hotels & Resorts' 'The Hershey Company'
 'Humana Inc.' 'International Business Machines'
 'Intl Flavors & Fragrances' 'International Paper' 'Interpublic Group'
 'Iron Mountain Incorporated' 'Illinois Tool Works' 'Invesco Ltd.'
 'J. B. Hunt Transport Services' 'Jacobs Engineering Group'
 'Juniper Networks' 'Kimco Realty' 'Leggett & Platt' 'Lennar Corp.'
 'Laboratory Corp. of America Holding' 'LKQ Corporation'
 'L-3 Communications Holdings' 'Lilly (Eli) & Co.' 'Alliant Energy Corp'
 'Leucadia National Corp.' 'Southwest Airlines' 'Level 3 Communications'
 'LyondellBasell' 'Mastercard Inc.' 'Mid-America Apartments' 'Macerich'
 "Marriott Int'l." 'Mattel Inc.' "Moody's Corp" 'Mondelez International'
 'MetLife Inc.' 'Mohawk Industries' 'Mead Johnson' 'McCormick & Co.'
 'Marsh & McLennan' '3M Company' 'Marathon Petroleum' 'M&T Bank Corp.'
 'Mylan N.V.' 'Navient' 'NASDAQ OMX Group' 'NextEra Energy'
 'Nielsen Holdings' 'Norfolk Southern Corp.' 'Northern Trust Corp.'
 'Nucor Corp.' 'Newell Brands' 'Realty Income Corporation' 'Omnicom Group'
 "O'Reilly Automotive" "People's United Financial" 'PACCAR Inc.'
 'PG&E Corp.' 'Public Serv. Enterprise Inc.' 'PepsiCo Inc.'
 'Principal Financial Group' 'Procter & Gamble' 'Progressive Corp.'
 'Pulte Homes Inc.' 'Philip Morris International' 'PNC Financial Services'
 'Pentair Ltd.' 'Pinnacle West Capital' 'PPG Industries' 'PPL Corp.'
 'Prudential Financial' 'Phillips 66' 'Praxair Inc.' 'PayPal'
 'Royal Caribbean Cruises Ltd' 'Robert Half International'
 'Roper Industries' 'Republic Services Inc' 'SCANA Corp'
 'Charles Schwab Corporation' 'Sealed Air' 'Sherwin-Williams'
 'SL Green Realty' 'Scripps Networks Interactive Inc.' 'Southern Co.'
 'Simon Property Group Inc' 'Stericycle Inc' 'Sempra Energy'
 'SunTrust Banks' 'State Street Corp.' 'Skyworks Solutions'
 'Synchrony Financial' 'Stryker Corp.' 'Molson Coors Brewing Company'
 'Tegna, Inc.' 'Torchmark Corp.' 'Thermo Fisher Scientific'
 'The Travelers Companies Inc.' 'Tractor Supply Company' 'Tyson Foods'
 'Tesoro Petroleum Co.' 'Total System Services' 'Texas Instruments'
 'Under Armour' 'UDR Inc' 'Universal Health Services, Inc.'
 'United Health Group Inc.' 'Unum Group' 'Union Pacific'
 'United Technologies' 'Varian Medical Systems' 'Valero Energy'
 'Vulcan Materials' 'Vornado Realty Trust' 'Verisk Analytics'
 'Verisign Inc.' 'Ventas Inc' 'Wec Energy Group Inc'
 'Waste Management Inc.' 'Western Union Co' 'Weyerhaeuser Corp.'
 'Wyndham Worldwide' 'Xcel Energy Inc' 'XL Capital' 'Dentsply Sirona'
 'Xerox Corp.' 'Xylem Inc.' 'Yum! Brands Inc' 'Zimmer Biomet Holdings'
 'Zions Bancorp' 'Zoetis']

In cluster 0, the following companies are present:
['Analog Devices, Inc.' 'Amgen Inc' 'Celgene Corp.' 'eBay Inc.'
 'Edwards Lifesciences' 'Facebook' 'First Solar Inc'
 'Frontier Communications' "McDonald's Corp." 'Monster Beverage'
 'Newmont Mining Corp. (Hldg. Co.)' 'TripAdvisor'
 'Vertex Pharmaceuticals Inc' 'Waters Corporation' 'Wynn Resorts Ltd'
 'Yahoo Inc.']

In cluster 4, the following companies are present:
['Alliance Data Systems' 'BIOGEN IDEC Inc.' 'Chipotle Mexican Grill'
 'Equinix' 'Intuitive Surgical Inc.' 'Mettler Toledo' 'Priceline.com Inc'
 'Regeneron']

In cluster 7, the following companies are present:
['Alexion Pharmaceuticals' 'Amazon.com Inc' 'Netflix Inc.']

In cluster 5, the following companies are present:
['Apache Corporation' 'Chesapeake Energy']

In cluster 1, the following companies are present:
['Anadarko Petroleum Corp' 'Devon Energy Corp.' 'EOG Resources'
 'Freeport-McMoran Cp & Gld' 'Hess Corporation' 'Murphy Oil'
 'Newfield Exploration Co' 'Occidental Petroleum' 'Cimarex Energy']

In cluster 8, the following companies are present:
['Arconic Inc' 'Baker Hughes Inc' 'CF Industries Holdings Inc'
 'Cabot Oil & Gas' 'Concho Resources' 'EQT Corporation' 'Halliburton Co.'
 'Hewlett Packard Enterprise' 'Kinder Morgan' 'Kansas City Southern'
 'Martin Marietta Materials' 'The Mosaic Company' 'Marathon Oil Corp.'
 'Noble Energy Inc' 'National Oilwell Varco Inc.' 'ONEOK'
 'Quanta Services Inc.' 'Ryder System' 'Range Resources Corp.'
 'Spectra Energy Corp.' 'Southwestern Energy' 'Teradata Corp.'
 'Williams Cos.']

In cluster 3, the following companies are present:
['Bank of America Corp' 'Citigroup Inc.' 'Ford Motor' 'Gilead Sciences'
 'Intel Corp.' 'JPMorgan Chase & Co.' 'Coca Cola Company' 'Merck & Co.'
 'Pfizer Inc.' 'AT&T Inc' 'Verizon Communications' 'Wells Fargo'
 'Exxon Mobil Corp.']


df1.groupby(["KM_segments", "GICS Sector"])['Security'].count()


plt.figure(figsize=(20, 20))
plt.suptitle("Boxplot of numerical variables for each cluster")

# selecting numerical columns
num_col = df.select_dtypes(include=np.number).columns.tolist()

for i, variable in enumerate(num_col):
    plt.subplot(3, 4, i + 1)
    sns.boxplot(data=df1, x="KM_segments", y=variable)

plt.tight_layout(pad=2.0)


hc_df = subset_scaled_df.copy()


# list of distance metrics
distance_metrics = ["euclidean", "chebyshev", "mahalanobis", "cityblock"] ## Complete the code to add distance metrics

# list of linkage methods
linkage_methods = ["single", "complete", "average", "weighted"] ## Complete the code to add linkages

high_cophenet_corr = 0
high_dm_lm = [0, 0]

for dm in distance_metrics:
    for lm in linkage_methods:
        Z = linkage(hc_df, metric=dm, method=lm)
        c, coph_dists = cophenet(Z, pdist(hc_df))
        print(
            "Cophenetic correlation for {} distance and {} linkage is {}.".format(
                dm.capitalize(), lm, c
            )
        )
        if high_cophenet_corr < c:
            high_cophenet_corr = c
            high_dm_lm[0] = dm
            high_dm_lm[1] = lm

# printing the combination of distance metric and linkage method with the highest cophenetic correlation
print('*'*100)
print(
    "Highest cophenetic correlation is {}, which is obtained with {} distance and {} linkage.".format(
        high_cophenet_corr, high_dm_lm[0].capitalize(), high_dm_lm[1]
    )
)

Cophenetic correlation for Euclidean distance and single linkage is 0.9232271494002922.
Cophenetic correlation for Euclidean distance and complete linkage is 0.7873280186580672.
Cophenetic correlation for Euclidean distance and average linkage is 0.9422540609560814.
Cophenetic correlation for Euclidean distance and weighted linkage is 0.8693784298129404.
Cophenetic correlation for Chebyshev distance and single linkage is 0.9062538164750717.
Cophenetic correlation for Chebyshev distance and complete linkage is 0.598891419111242.
Cophenetic correlation for Chebyshev distance and average linkage is 0.9338265528030499.
Cophenetic correlation for Chebyshev distance and weighted linkage is 0.9127355892367.
Cophenetic correlation for Mahalanobis distance and single linkage is 0.925919553052459.
Cophenetic correlation for Mahalanobis distance and complete linkage is 0.7925307202850002.
Cophenetic correlation for Mahalanobis distance and average linkage is 0.9247324030159736.
Cophenetic correlation for Mahalanobis distance and weighted linkage is 0.8708317490180428.
Cophenetic correlation for Cityblock distance and single linkage is 0.9334186366528574.
Cophenetic correlation for Cityblock distance and complete linkage is 0.7375328863205818.
Cophenetic correlation for Cityblock distance and average linkage is 0.9302145048594667.
Cophenetic correlation for Cityblock distance and weighted linkage is 0.731045513520281.
****************************************************************************************************
Highest cophenetic correlation is 0.9422540609560814, which is obtained with Euclidean distance and average linkage.


# list of linkage methods
linkage_methods = ["single", "complete", "average", "centroid", "ward", "weighted"]

high_cophenet_corr = 0
high_dm_lm = [0, 0]

for lm in linkage_methods:
    Z = linkage(hc_df, metric="euclidean", method=lm)
    c, coph_dists = cophenet(Z, pdist(hc_df))
    print("Cophenetic correlation for {} linkage is {}.".format(lm, c))
    if high_cophenet_corr < c:
        high_cophenet_corr = c
        high_dm_lm[0] = "euclidean"
        high_dm_lm[1] = lm

# printing the combination of distance metric and linkage method with the highest cophenetic correlation
print('*'*100)
print(
    "Highest cophenetic correlation is {}, which is obtained with {} linkage.".format(
        high_cophenet_corr, high_dm_lm[1]
    )
)

Cophenetic correlation for single linkage is 0.9232271494002922.
Cophenetic correlation for complete linkage is 0.7873280186580672.
Cophenetic correlation for average linkage is 0.9422540609560814.
Cophenetic correlation for centroid linkage is 0.9314012446828154.
Cophenetic correlation for ward linkage is 0.7101180299865353.
Cophenetic correlation for weighted linkage is 0.8693784298129404.
****************************************************************************************************
Highest cophenetic correlation is 0.9422540609560814, which is obtained with average linkage.


# list of linkage methods
linkage_methods = ["single", "complete", "average", "centroid", "ward", "weighted"]

# lists to save results of cophenetic correlation calculation
compare_cols = ["Linkage", "Cophenetic Coefficient"]
compare = []

# to create a subplot image
fig, axs = plt.subplots(len(linkage_methods), 1, figsize=(15, 30))

# We will enumerate through the list of linkage methods above
# For each linkage method, we will plot the dendrogram and calculate the cophenetic correlation
for i, method in enumerate(linkage_methods):
    Z = linkage(hc_df, metric="euclidean", method=method)

    dendrogram(Z, ax=axs[i])
    axs[i].set_title(f"Dendrogram ({method.capitalize()} Linkage)")

    coph_corr, coph_dist = cophenet(Z, pdist(hc_df))
    axs[i].annotate(
        f"Cophenetic\nCorrelation\n{coph_corr:0.2f}",
        (0.80, 0.80),
        xycoords="axes fraction",
    )

    compare.append([method, coph_corr])


# create and print a dataframe to compare cophenetic correlations for different linkage methods
df_cc = pd.DataFrame(compare, columns=compare_cols)
df_cc = df_cc.sort_values(by="Cophenetic Coefficient")
df_cc


from sklearn.cluster import AgglomerativeClustering

HCmodel = AgglomerativeClustering(n_clusters=6, metric="euclidean", linkage="ward")
HCmodel.fit(hc_df)

AgglomerativeClustering(n_clusters=6)

AgglomerativeClustering(n_clusters=6)


# creating a copy of the original data
df2 = df.copy()

# adding hierarchical cluster labels to the original and scaled dataframes
hc_df["HC_segments"] = HCmodel.labels_
df2["HC_segments"] = HCmodel.labels_


# Select only numeric columns and then calculate the mean for each cluster
hc_cluster_profile = df2.select_dtypes(include='number').groupby(df2["HC_segments"]).mean()


hc_cluster_profile["count_in_each_segment"] = (
    df2.groupby("HC_segments")["Security"].count().values  ## Complete the code to groupby the cluster labels
)


hc_cluster_profile.style.highlight_max(color="yellow", axis=0)


## Complete the code to print the companies in each cluster
for cl in df2["HC_segments"].unique():
    print("In cluster {}, the following companies are present:".format(cl))
    print(df2[df2["HC_segments"] == cl]["Security"].unique())
    print()

In cluster 3, the following companies are present:
['American Airlines Group' 'AbbVie' 'Abbott Laboratories'
 'Adobe Systems Inc' 'Analog Devices, Inc.' 'Archer-Daniels-Midland Co'
 'Ameren Corp' 'American Electric Power' 'AFLAC Inc'
 'American International Group, Inc.' 'Apartment Investment & Mgmt'
 'Assurant Inc' 'Arthur J. Gallagher & Co.' 'Akamai Technologies Inc'
 'Albemarle Corp' 'Alaska Air Group Inc' 'Allstate Corp'
 'Applied Materials Inc' 'AMETEK Inc' 'Affiliated Managers Group Inc'
 'Ameriprise Financial' 'American Tower Corp A' 'AutoNation Inc'
 'Anthem Inc.' 'Aon plc' 'Amphenol Corp' 'Arconic Inc'
 'Activision Blizzard' 'AvalonBay Communities, Inc.' 'Broadcom'
 'American Water Works Company Inc' 'American Express Co' 'Boeing Company'
 'Baxter International Inc.' 'BB&T Corporation' 'Bard (C.R.) Inc.'
 'BIOGEN IDEC Inc.' 'The Bank of New York Mellon Corp.' 'Ball Corp'
 'Bristol-Myers Squibb' 'Boston Scientific' 'BorgWarner'
 'Boston Properties' 'Caterpillar Inc.' 'Chubb Limited' 'CBRE Group'
 'Crown Castle International Corp.' 'Carnival Corp.' 'Celgene Corp.'
 'CF Industries Holdings Inc' 'Citizens Financial Group' 'Church & Dwight'
 'C. H. Robinson Worldwide' 'CIGNA Corp.' 'Cincinnati Financial'
 'Comerica Inc.' 'CME Group Inc.' 'Cummins Inc.' 'CMS Energy'
 'Centene Corporation' 'CenterPoint Energy' 'Capital One Financial'
 'The Cooper Companies' 'CSX Corp.' 'CenturyLink Inc'
 'Cognizant Technology Solutions' 'Citrix Systems' 'CVS Health'
 'Chevron Corp.' 'Dominion Resources' 'Delta Air Lines' 'Du Pont (E.I.)'
 'Deere & Co.' 'Discover Financial Services' 'Quest Diagnostics'
 'Danaher Corp.' 'The Walt Disney Company' 'Discovery Communications-A'
 'Discovery Communications-C' 'Delphi Automotive' 'Digital Realty Trust'
 'Dun & Bradstreet' 'Dover Corp.' 'Dr Pepper Snapple Group' 'Duke Energy'
 'DaVita Inc.' 'eBay Inc.' 'Ecolab Inc.' 'Consolidated Edison'
 'Equifax Inc.' "Edison Int'l" 'Eastman Chemical' 'Equity Residential'
 'EQT Corporation' 'Eversource Energy' 'Essex Property Trust, Inc.'
 'E*Trade' 'Eaton Corporation' 'Entergy Corp.' 'Edwards Lifesciences'
 'Exelon Corp.' "Expeditors Int'l" 'Expedia Inc.' 'Extra Space Storage'
 'Fastenal Co' 'Fortune Brands Home & Security' 'FirstEnergy Corp'
 'Fidelity National Information Services' 'Fiserv Inc' 'FLIR Systems'
 'Fluor Corp.' 'Flowserve Corporation' 'FMC Corporation'
 'Federal Realty Investment Trust' 'First Solar Inc' 'General Dynamics'
 'General Growth Properties Inc.' 'Gilead Sciences' 'Corning Inc.'
 'General Motors' 'Genuine Parts' 'Garmin Ltd.' 'Goodyear Tire & Rubber'
 'Grainger (W.W.) Inc.' 'Halliburton Co.' 'Hasbro Inc.'
 'Huntington Bancshares' 'HCA Holdings' 'Welltower Inc.' 'HCP Inc.'
 'Hartford Financial Svc.Gp.' 'Harley-Davidson' "Honeywell Int'l Inc."
 'Hewlett Packard Enterprise' 'HP Inc.' 'Hormel Foods Corp.'
 'Henry Schein' 'Host Hotels & Resorts' 'The Hershey Company'
 'Humana Inc.' 'International Business Machines' 'IDEXX Laboratories'
 'Intl Flavors & Fragrances' 'International Paper' 'Interpublic Group'
 'Iron Mountain Incorporated' 'Illinois Tool Works' 'Invesco Ltd.'
 'J. B. Hunt Transport Services' 'Jacobs Engineering Group'
 'Juniper Networks' 'Kimco Realty' 'Kansas City Southern'
 'Leggett & Platt' 'Lennar Corp.' 'Laboratory Corp. of America Holding'
 'LKQ Corporation' 'L-3 Communications Holdings' 'Lilly (Eli) & Co.'
 'Lockheed Martin Corp.' 'Alliant Energy Corp' 'Leucadia National Corp.'
 'Southwest Airlines' 'Level 3 Communications' 'LyondellBasell'
 'Mastercard Inc.' 'Mid-America Apartments' 'Macerich' "Marriott Int'l."
 'Masco Corp.' 'Mattel Inc.' "McDonald's Corp." "Moody's Corp"
 'Mondelez International' 'MetLife Inc.' 'Mohawk Industries'
 'Mead Johnson' 'McCormick & Co.' 'Martin Marietta Materials'
 'Marsh & McLennan' '3M Company' 'Altria Group Inc' 'The Mosaic Company'
 'Marathon Petroleum' 'Merck & Co.' 'M&T Bank Corp.' 'Mettler Toledo'
 'Mylan N.V.' 'Navient' 'NASDAQ OMX Group' 'NextEra Energy'
 'Newmont Mining Corp. (Hldg. Co.)' 'Nielsen Holdings'
 'Norfolk Southern Corp.' 'Northern Trust Corp.' 'Nucor Corp.'
 'Newell Brands' 'Realty Income Corporation' 'Omnicom Group'
 "O'Reilly Automotive" "People's United Financial" 'Pitney-Bowes'
 'PACCAR Inc.' 'PG&E Corp.' 'Public Serv. Enterprise Inc.' 'PepsiCo Inc.'
 'Principal Financial Group' 'Procter & Gamble' 'Progressive Corp.'
 'Pulte Homes Inc.' 'Philip Morris International' 'PNC Financial Services'
 'Pentair Ltd.' 'Pinnacle West Capital' 'PPG Industries' 'PPL Corp.'
 'Prudential Financial' 'Phillips 66' 'Quanta Services Inc.'
 'Praxair Inc.' 'PayPal' 'Ryder System' 'Royal Caribbean Cruises Ltd'
 'Robert Half International' 'Roper Industries' 'Republic Services Inc'
 'SCANA Corp' 'Charles Schwab Corporation' 'Sealed Air' 'Sherwin-Williams'
 'SL Green Realty' 'Scripps Networks Interactive Inc.' 'Southern Co.'
 'Simon Property Group Inc' 'Stericycle Inc' 'Sempra Energy'
 'SunTrust Banks' 'State Street Corp.' 'Skyworks Solutions'
 'Synchrony Financial' 'Stryker Corp.' 'Molson Coors Brewing Company'
 'Tegna, Inc.' 'Torchmark Corp.' 'Thermo Fisher Scientific' 'TripAdvisor'
 'The Travelers Companies Inc.' 'Tractor Supply Company' 'Tyson Foods'
 'Tesoro Petroleum Co.' 'Total System Services' 'Texas Instruments'
 'Under Armour' 'United Continental Holdings' 'UDR Inc'
 'Universal Health Services, Inc.' 'United Health Group Inc.' 'Unum Group'
 'Union Pacific' 'United Parcel Service' 'United Technologies'
 'Varian Medical Systems' 'Valero Energy' 'Vulcan Materials'
 'Vornado Realty Trust' 'Verisk Analytics' 'Verisign Inc.'
 'Vertex Pharmaceuticals Inc' 'Ventas Inc' 'Wec Energy Group Inc'
 'Whirlpool Corp.' 'Waste Management Inc.' 'Western Union Co'
 'Weyerhaeuser Corp.' 'Wyndham Worldwide' 'Wynn Resorts Ltd'
 'Xcel Energy Inc' 'XL Capital' 'Dentsply Sirona' 'Xerox Corp.'
 'Xylem Inc.' 'Yum! Brands Inc' 'Zimmer Biomet Holdings' 'Zions Bancorp'
 'Zoetis']

In cluster 2, the following companies are present:
['Alliance Data Systems' 'Amgen Inc' 'Chipotle Mexican Grill' 'Equinix'
 'Facebook' 'Frontier Communications' 'Intuitive Surgical Inc.'
 'Monster Beverage' 'Priceline.com Inc' 'Regeneron' 'Waters Corporation'
 'Yahoo Inc.']

In cluster 1, the following companies are present:
['Allegion' 'Apache Corporation' 'Chesapeake Energy'
 'Charter Communications' 'Colgate-Palmolive' 'Kimberly-Clark'
 'S&P Global, Inc.']

In cluster 5, the following companies are present:
['Alexion Pharmaceuticals' 'Amazon.com Inc' 'Netflix Inc.']

In cluster 4, the following companies are present:
['Anadarko Petroleum Corp' 'Baker Hughes Inc' 'Cabot Oil & Gas'
 'Concho Resources' 'Devon Energy Corp.' 'EOG Resources'
 'Freeport-McMoran Cp & Gld' 'Hess Corporation' 'Kinder Morgan'
 'Marathon Oil Corp.' 'Murphy Oil' 'Noble Energy Inc'
 'Newfield Exploration Co' 'National Oilwell Varco Inc.' 'ONEOK'
 'Occidental Petroleum' 'Range Resources Corp.' 'Spectra Energy Corp.'
 'Southwestern Energy' 'Teradata Corp.' 'Williams Cos.' 'Cimarex Energy']

In cluster 0, the following companies are present:
['Bank of America Corp' 'Citigroup Inc.' 'Ford Motor' 'Intel Corp.'
 'JPMorgan Chase & Co.' 'Coca Cola Company' 'Pfizer Inc.' 'AT&T Inc'
 'Verizon Communications' 'Wells Fargo' 'Exxon Mobil Corp.']


df2.groupby(["HC_segments", "GICS Sector"])['Security'].count()


plt.figure(figsize=(20, 20))
plt.suptitle("Boxplot of numerical variables for each cluster")

for i, variable in enumerate(num_col):
    plt.subplot(3, 4, i + 1)
    sns.boxplot(data=df2, x="HC_segments", y=variable)

plt.tight_layout(pad=2.0)

	Ticker Symbol	Security	GICS Sector	GICS Sub Industry	Current Price	Price Change	Volatility	ROE	Cash Ratio	Net Cash Flow	Net Income	Earnings Per Share	Estimated Shares Outstanding	P/E Ratio	P/B Ratio
0	AAL	American Airlines Group	Industrials	Airlines	42.349998	9.999995	1.687151	135	51	-604000000	7610000000	11.39	6.681299e+08	3.718174	-8.784219
1	ABBV	AbbVie	Health Care	Pharmaceuticals	59.240002	8.339433	2.197887	130	77	51000000	5144000000	3.15	1.633016e+09	18.806350	-8.750068
2	ABT	Abbott Laboratories	Health Care	Health Care Equipment	44.910000	11.301121	1.273646	21	67	938000000	4423000000	2.94	1.504422e+09	15.275510	-0.394171
3	ADBE	Adobe Systems Inc	Information Technology	Application Software	93.940002	13.977195	1.357679	9	180	-240840000	629551000	1.26	4.996437e+08	74.555557	4.199651
4	ADI	Analog Devices, Inc.	Information Technology	Semiconductors	55.320000	-1.827858	1.701169	14	272	315120000	696878000	0.31	2.247994e+09	178.451613	1.059810

	Ticker Symbol	Security	GICS Sector	GICS Sub Industry	Current Price	Price Change	Volatility	ROE	Cash Ratio	Net Cash Flow	Net Income	Earnings Per Share	Estimated Shares Outstanding	P/E Ratio	P/B Ratio
335	YHOO	Yahoo Inc.	Information Technology	Internet Software & Services	33.259998	14.887727	1.845149	15	459	-1032187000	-4359082000	-4.64	939457327.6	28.976191	6.261775
336	YUM	Yum! Brands Inc	Consumer Discretionary	Restaurants	52.516175	-8.698917	1.478877	142	27	159000000	1293000000	2.97	435353535.4	17.682214	-3.838260
337	ZBH	Zimmer Biomet Holdings	Health Care	Health Care Equipment	102.589996	9.347683	1.404206	1	100	376000000	147000000	0.78	188461538.5	131.525636	-23.884449
338	ZION	Zions Bancorp	Financials	Regional Banks	27.299999	-1.158588	1.468176	4	99	-43623000	309471000	1.20	257892500.0	22.749999	-0.063096
339	ZTS	Zoetis	Health Care	Pharmaceuticals	47.919998	16.678836	1.610285	32	65	272000000	339000000	0.68	498529411.8	70.470585	1.723068

	count	mean	std	min	25%	50%	75%	max
Current Price	340.0	8.086234e+01	9.805509e+01	4.500000e+00	3.855500e+01	5.970500e+01	9.288000e+01	1.274950e+03
Price Change	340.0	4.078194e+00	1.200634e+01	-4.712969e+01	-9.394838e-01	4.819505e+00	1.069549e+01	5.505168e+01
Volatility	340.0	1.525976e+00	5.917984e-01	7.331632e-01	1.134878e+00	1.385593e+00	1.695549e+00	4.580042e+00
ROE	340.0	3.959706e+01	9.654754e+01	1.000000e+00	9.750000e+00	1.500000e+01	2.700000e+01	9.170000e+02
Cash Ratio	340.0	7.002353e+01	9.042133e+01	0.000000e+00	1.800000e+01	4.700000e+01	9.900000e+01	9.580000e+02
Net Cash Flow	340.0	5.553762e+07	1.946365e+09	-1.120800e+10	-1.939065e+08	2.098000e+06	1.698108e+08	2.076400e+10
Net Income	340.0	1.494385e+09	3.940150e+09	-2.352800e+10	3.523012e+08	7.073360e+08	1.899000e+09	2.444200e+10
Earnings Per Share	340.0	2.776662e+00	6.587779e+00	-6.120000e+01	1.557500e+00	2.895000e+00	4.620000e+00	5.009000e+01
Estimated Shares Outstanding	340.0	5.770283e+08	8.458496e+08	2.767216e+07	1.588482e+08	3.096751e+08	5.731175e+08	6.159292e+09
P/E Ratio	340.0	3.261256e+01	4.434873e+01	2.935451e+00	1.504465e+01	2.081988e+01	3.176476e+01	5.280391e+02
P/B Ratio	340.0	-1.718249e+00	1.396691e+01	-7.611908e+01	-4.352056e+00	-1.067170e+00	3.917066e+00	1.290646e+02

	Price Change
GICS Sector
Health Care	9.585652
Consumer Staples	8.684750
Information Technology	7.217476
Telecommunications Services	6.956980
Real Estate	6.205548
Consumer Discretionary	5.846093
Materials	5.589738
Financials	3.865406
Industrials	2.833127
Utilities	0.803657
Energy	-10.228289

	Cash Ratio
GICS Sector
Consumer Discretionary	49.575000
Consumer Staples	70.947368
Energy	51.133333
Financials	98.591837
Health Care	103.775000
Industrials	36.188679
Information Technology	149.818182
Materials	41.700000
Real Estate	50.111111
Telecommunications Services	117.000000
Utilities	13.625000

	P/E Ratio
GICS Sector
Consumer Discretionary	35.211613
Consumer Staples	25.521195
Energy	72.897709
Financials	16.023151
Health Care	41.135272
Industrials	18.259380
Information Technology	43.782546
Materials	24.585352
Real Estate	43.065585
Telecommunications Services	12.222578
Utilities	18.719412

	Current Price	Price Change	Volatility	ROE	Cash Ratio	Net Cash Flow	Net Income	Earnings Per Share	Estimated Shares Outstanding	P/E Ratio	P/B Ratio
0	-0.393341	0.493950	0.272749	0.989601	-0.210698	-0.339355	1.554415	1.309399	0.107863	-0.652487	-0.506653
1	-0.220837	0.355439	1.137045	0.937737	0.077269	-0.002335	0.927628	0.056755	1.250274	-0.311769	-0.504205
2	-0.367195	0.602479	-0.427007	-0.192905	-0.033488	0.454058	0.744371	0.024831	1.098021	-0.391502	0.094941
3	0.133567	0.825696	-0.284802	-0.317379	1.218059	-0.152497	-0.219816	-0.230563	-0.091622	0.947148	0.424333
4	-0.260874	-0.492636	0.296470	-0.265515	2.237018	0.133564	-0.202703	-0.374982	1.978399	3.293307	0.199196

	Current Price	Price Change	Volatility	ROE	Cash Ratio	Net Cash Flow	Net Income	Earnings Per Share	Estimated Shares Outstanding	P/E Ratio	P/B Ratio	KM_segments	count_in_each_segment
KM_segments
0	78.317709	17.072784	1.837829	30.937500	355.000000	282351312.500000	1045009937.500000	2.041875	826652504.337500	43.575343	8.327790	0.000000	16
1	46.513334	-11.339513	2.528347	96.444444	57.888889	-896701333.333333	-6305921777.777778	-16.607778	445515335.960000	70.907059	0.959532	1.000000	9
2	70.786021	5.434831	1.353096	20.114286	53.379592	16310469.387755	1449401085.714286	3.535204	428851023.545633	23.820076	-1.770027	2.000000	245
3	48.103077	6.053507	1.163964	27.538462	77.230769	773230769.230769	14114923076.923077	3.958462	3918734987.169230	16.098039	-4.253404	3.000000	13
4	508.534992	5.732177	1.504640	27.250000	150.875000	37895875.000000	1116994125.000000	15.965000	75654420.935000	43.727459	29.581664	4.000000	8
5	24.485001	-13.351992	3.482611	802.000000	51.000000	-1292500000.000000	-19106500000.000000	-41.815000	519573983.250000	60.748608	1.565141	5.000000	2
6	90.797143	4.957495	1.462532	214.142857	40.380952	-93181142.857143	2195875523.809524	4.785714	443891905.006190	22.110362	-23.952239	6.000000	21
7	327.006671	21.917380	2.029752	4.000000	106.000000	698240666.666667	287547000.000000	0.750000	366763235.300000	400.989188	-5.322376	7.000000	3
8	36.894297	-16.685444	2.792467	21.000000	45.652174	457884000.000000	-355319304.347826	-0.154783	472830352.019130	68.222052	1.828094	8.000000	23

	Linkage	Cophenetic Coefficient
4	ward	0.710118
1	complete	0.787328
5	weighted	0.869378
0	single	0.923227
3	centroid	0.931401
2	average	0.942254

	Current Price	Price Change	Volatility	ROE	Cash Ratio	Net Cash Flow	Net Income	Earnings Per Share	Estimated Shares Outstanding	P/E Ratio	P/B Ratio	HC_segments	count_in_each_segment
HC_segments
0	42.848182	6.270446	1.123547	22.727273	71.454545	558636363.636364	14631272727.272728	3.410000	4242572567.290909	15.242169	-4.924615	0.000000	11
1	84.355716	3.854981	1.827670	633.571429	33.571429	-568400000.000000	-4968157142.857142	-10.841429	398169036.442857	42.284541	-11.589502	1.000000	7
2	325.996105	7.724708	1.545762	17.000000	360.333333	186503166.666667	1008736416.666667	9.544167	589434517.801667	41.121871	25.258283	2.000000	12
3	72.760400	5.213307	1.427078	25.603509	60.392982	79951512.280702	1538594322.807018	3.655351	446472132.228456	24.722670	-2.647194	3.000000	285
4	36.440455	-16.073408	2.832884	57.500000	42.409091	-472834090.909091	-3161045227.272727	-8.005000	514367806.201818	85.555682	0.836839	4.000000	22
5	327.006671	21.917380	2.029752	4.000000	106.000000	698240666.666667	287547000.000000	0.750000	366763235.300000	400.989188	-5.322376	5.000000	3

Context¶

Objective¶

Data Description¶

Data Dictionary¶

Libraries¶

Import Data¶

Dataset Analysis¶

Statistical summary¶

Exploratory Data Analysis¶

Univariate analysis¶

Ticker symbol¶

Company¶

Current price¶

Volatility¶

ROE¶

Cash ratio¶

Net cash flow¶

Net income¶

Earning per share¶

Estimated shares outstanding¶

P/E ratio¶

P/B ratio¶

GICS Sector¶

GICS Sub Industry¶

Bivariate Analysis¶

Net Income vs. Earnings Per Share (EPS)¶

Earnings Per Share vs. Current Price¶

Net Income vs. Estimated Shares Outstanding¶

Volatility vs. Price Change¶

ROE vs. Earnings Per Share¶

Current Price vs. P/E Ratio¶

P/B Ratio vs. Cash Ratio¶

Volatility vs. GICS Sector¶

Net Cash Flow vs. Cash Ratio¶

Price Change vs. GICS Sector¶

P/E Ratio vs. Volatility¶

Price Change vs. ROE¶

GICS Sector vs Price change¶

GICS sector vs P/E Ratio¶

GICS Sector vs Volatility¶

Multivariate analysis¶

ROE, Cash Ratio, and Net Income¶

Net Cash Flow, Cash Ratio, and P/B Ratio¶

Volatility, ROE, and Net Income¶

GICS Sector, Price Change, and Current Price¶

EDA Observations¶

Exploratory Data Analysis Questions¶

Question no:1¶

Question no:2¶

Question no:3¶

Question no:4¶

Question no:5¶

Data Processing¶

Duplicate value check¶

Missing values and Treatment¶

Outlier Check¶

Scaling¶

K-means Clustering¶

silhouette scores¶

Final model¶

Cluster profiling¶

Hierarchical Clustering¶

K-means vs Hierarchical Clustering¶

Actionable insights and Recommendations¶

Actionable Insights¶

Recommendations¶